Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm

Abstract

Abstract Skin cancer, particularly melanoma, poses a critical global health challenge due to its high mortality rate. Early and precise detection is vital for effective treatment and better prognosis. Recent advancements in deep learning have shown significant promise in medical image analysis, including skin cancer classification. This study investigates the automated classification of skin lesions using the HAM10000 dataset, which features high-resolution images across seven distinct classes. We focus on utilizing deep learning, specifically convolutional neural networks (CNNs), to enhance the accuracy of skin lesion classification. Our research examines several CNN architectures, including XceptionNet, DenseNet201, DenseNet169, DenseNet121, MobileNetV2, and GoogleNet, alongside a customized CNN model tailored for skin cancer classification. We incorporate techniques such as data augmentation and transfer learning to further refine model performance. Hyperparameter optimization is achieved using the Ant Colony Optimization algorithm. The proposed models are evaluated on the HAM10000 dataset with standard metrics; accuracy, precision, recall, and F1-score. Our results highlight the effectiveness of deep learning in distinguishing between various skin cancer types attaining values of 96.5%, 97.0%, and 97.0% for accuracy, precision, recall, and F1-score, respectively, showing improvements over existing state-of-the-art methods in both classification accuracy. These findings offer significant implications for dermatology and healthcare by facilitating automated skin cancer classification, potentially aiding dermatologists in early diagnosis and improving patient outcomes. Additionally, this framework provides a foundation for future research in applying deep learning to medical image analysis and healthcare diagnostics.